OpenRaiser/Intern-Atlas huggingface dataset:
Recognizing Topic Change in Search Sessions of Digital Libraries based on Thesaurus and Classification System
Venue: arXiv cs.DL 2019
Year: 2019

Log analysis in Web search showed that user sessions often contain several different topics. This means sessions need to be segmented into parts which handle the same topic in order to give appropriate user support based on the topic, and not on a mixture of topics. Different methods have been proposed to segment a user session to different topics based on timeouts, lexical analysis, query similarity or external knowledge sources. In this paper, we study the problem in a digital library for the social sciences. We present a method based on a thesaurus and a classification system which are typical knowledge organization systems in digital libraries. Five experts evaluated our approach and rated it as good for the segmentation of search sessions into parts that treat the same topic.
An Empirical Study of Content Understanding in Conversational Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

With a lot of work about context-free question answering systems, there is an emerging trend of conversational question answering models in the natural language processing field. Thanks to the recently collected datasets, including QuAC and CoQA, there has been more work on conversational question answering, and recent work has achieved competitive performance on both datasets. However, to best of our knowledge, two important questions for conversational comprehension research have not been well studied: 1) How well can the benchmark dataset reflect models' content understanding? 2) Do the models well utilize the conversation content when answering questions? To investigate these questions, we design different training settings, testing settings, as well as an attack to verify the models' capability of content understanding on QuAC and CoQA. The experimental results indicate some potential hazards in the benchmark datasets, QuAC and CoQA, for conversational comprehension research. Our analysis also sheds light on both what models may learn and how datasets may bias the models. With deep investigation of the task, it is believed that this work can benefit the future progress of conversation comprehension. The source code is available at https://github.com/MiuLab/CQA-Study.
Distortion Estimation Through Explicit Modeling of the Refractive Surface
Venue: arXiv cs.CV 2019
Year: 2019

Precise calibration is a must for high reliance 3D computer vision algorithms. A challenging case is when the camera is behind a protective glass or transparent object: due to refraction, the image is heavily distorted; the pinhole camera model alone can not be used and a distortion correction step is required. By directly modeling the geometry of the refractive media, we build the image generation process by tracing individual light rays from the camera to a target. Comparing the generated images to their distorted - observed - counterparts, we estimate the geometry parameters of the refractive surface via model inversion by employing an RBF neural network. We present an image collection methodology that produces data suited for finding the distortion parameters and test our algorithm on synthetic and real-world data. We analyze the results of the algorithm.
Single Camera Training for Person Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

Person re-identification (ReID) aims at finding the same person in different cameras. Training such systems usually requires a large amount of cross-camera pedestrians to be annotated from surveillance videos, which is labor-consuming especially when the number of cameras is large. Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera. To the best of our knowledge, this setting was never studied before. SCT enjoys the advantage of low-cost data collection and annotation, and thus eases ReID systems to be trained in a brand new environment. However, it raises major challenges due to the lack of cross-camera person occurrences, which conventional approaches heavily rely on to extract discriminative features. The key to dealing with the challenges in the SCT setting lies in designing an effective mechanism to complement cross-camera annotation. We start with a regular deep network for feature extraction, upon which we propose a novel loss function named multi-camera negative loss (MCNL). This is a metric learning loss motivated by probability, suggesting that in a multi-camera system, one image is more likely to be closer to the most similar negative sample in other cameras than to the most similar negative sample in the same camera. In experiments, MCNL significantly boosts ReID accuracy in the SCT setting, which paves the way of fast deployment of ReID systems with good performance on new target scenes.
Restyling Data: Application to Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2019
Year: 2019

Machine learning is driven by data, yet while their availability is constantly increasing, training data require laborious, time consuming and error-prone labelling or ground truth acquisition, which in some cases is very difficult or even impossible. Recent works have resorted to synthetic data generation, but the inferior performance of models trained on synthetic data when applied to the real world, introduced the challenge of unsupervised domain adaptation. In this work we investigate an unsupervised domain adaptation technique that descends from another perspective, in order to avoid the complexity of adversarial training and cycle consistencies. We exploit the recent advances in photorealistic style transfer and take a fully data driven approach. While this concept is already implicitly formulated within the intricate objectives of domain adaptation GANs, we take an explicit approach and apply it directly as data pre-processing. The resulting technique is scalable, efficient and easy to implement, offers competitive performance to the complex state-of-the-art alternatives and can open up new pathways for domain adaptation.
A System-Level Solution for Low-Power Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Object detection has made impressive progress in recent years with the help of deep learning. However, state-of-the-art algorithms are both computation and memory intensive. Though many lightweight networks are developed for a trade-off between accuracy and efficiency, it is still a challenge to make it practical on an embedded device. In this paper, we present a system-level solution for efficient object detection on a heterogeneous embedded device. The detection network is quantized to low bits and allows efficient implementation with shift operators. In order to make the most of the benefits of low-bit quantization, we design a dedicated accelerator with programmable logic. Inside the accelerator, a hybrid dataflow is exploited according to the heterogeneous property of different convolutional layers. We adopt a straightforward but resource-friendly column-prior tiling strategy to map the computation-intensive convolutional layers to the accelerator that can support arbitrary feature size. Other operations can be performed on the low-power CPU cores, and the entire system is executed in a pipelined manner. As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6.9W (with display) with an mAP of 66.4 verified on the PASCAL VOC 2012 dataset.
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
Venue: arXiv cs.LG 2019
Year: 2019

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.
Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting
Venue: arXiv cs.CR 2019
Year: 2019

While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incor-porate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use crypto-graphic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS.
A Rule-Based Relational XML Access Control Model in the Presence of Authorization Conflicts
Venue: arXiv cs.DB 2019
Year: 2019

There is considerable amount of sensitive XML data stored in relational databases. It is a challenge to enforce node level fine-grained authorization policies for XML data stored in relational databases which typically support table and column level access control. Moreover, it is common to have conflicting authorization policies over the hierarchical nested structure of XML data. There are a couple of XML access control models for relational XML databases proposed in the literature. However, to our best knowledge, none of them discussed handling authorization conflicts with conditions in the domain of relational XML databases. Therefore, we believe that there is a need to define and incorporate effective fine-grained XML authorization models with conflict handling mechanisms in the presence of conditions into relational XML databases. We address this issue in this study.
Conditional Hardness of Earth Mover Distance
Venue: arXiv cs.CC 2019
Year: 2019

The Earth Mover Distance (EMD) between two sets of points $A, B \subseteq \mathbb{R}^d$ with $|A| = |B|$ is the minimum total Euclidean distance of any perfect matching between $A$ and $B$. One of its generalizations is asymmetric EMD, which is the minimum total Euclidean distance of any matching of size $|A|$ between sets of points $A,B \subseteq \mathbb{R}^d$ with $|A| \leq |B|$. The problems of computing EMD and asymmetric EMD are well-studied and have many applications in computer science, some of which also ask for the EMD-optimal matching itself. Unfortunately, all known algorithms require at least quadratic time to compute EMD exactly. Approximation algorithms with nearly linear time complexity in $n$ are known (even for finding approximately optimal matchings), but suffer from exponential dependence on the dimension. In this paper we show that significant improvements in exact and approximate algorithms for EMD would contradict conjectures in fine-grained complexity. In particular, we prove the following results: (1) Under the Orthogonal Vectors Conjecture, there is some $c>0$ such that EMD in $Ω(c^{\log^* n})$ dimensions cannot be computed in truly subquadratic time. (2) Under the Hitting Set Conjecture, for every $δ>0$, no truly subquadratic time algorithm can find a $(1 + 1/n^δ)$-approximate EMD matching in $ω(\log n)$ dimensions. (3) Under the Hitting Set Conjecture, for every $η= 1/ω(\log n)$, no truly subquadratic time algorithm can find a $(1 + η)$-approximate asymmetric EMD matching in $ω(\log n)$ dimensions.
A common approximation framework for the early work, the late work, and resource leveling problems with unit time jobs
Venue: arXiv cs.DS 2019
Year: 2019

We study the approximability of two related machine scheduling problems. In the late work minimization problem, there are identical parallel machines and the jobs have a common due date. The objective is to minimize the late work, defined as the sum of the portion of the jobs done after the due date. A related problem is the maximization of the early work, defined as the sum of the portion of the jobs done before the due date. We describe a polynomial time approximation scheme for the early work maximization problem, and we extended it to the late work minimization problem after shifting the objective function by a positive value that depends on the problem data. We also prove an inapproximability result for the latter problem if the objective function is shifted by a constant which does not depend on the input. These results remain valid even if the number of the jobs assigned to the same machine is bounded. This leads to an extension of our approximation scheme to some variants of the resource leveling problem, for which no approximation algorithms were known.
Leveraging the Template and Anchor Framework for Safe, Online Robotic Gait Design
Venue: arXiv cs.RO 2019
Year: 2019

Online control design using a high-fidelity, full-order model for a bipedal robot can be challenging due to the size of the state space of the model. A commonly adopted solution to overcome this challenge is to approximate the full-order model (anchor) with a simplified, reduced-order model (template), while performing control synthesis. Unfortunately it is challenging to make formal guarantees about the safety of an anchor model using a controller designed in an online fashion using a template model. To address this problem, this paper proposes a method to generate safety-preserving controllers for anchor models by performing reachability analysis on template models while bounding the modeling error. This paper describes how this reachable set can be incorporated into a Model Predictive Control framework to select controllers that result in safe walking on the anchor model in an online fashion. The method is illustrated on a 5-link RABBIT model, and is shown to allow the robot to walk safely while utilizing controllers designed in an online fashion.
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
Venue: arXiv cs.NE 2019
Year: 2019

Future developments in artificial intelligence will profit from the existence of novel, non-traditional substrates for brain-inspired computing. Neuromorphic computers aim to provide such a substrate that reproduces the brain's capabilities in terms of adaptive, low-power information processing. We present results from a prototype chip of the BrainScaleS-2 mixed-signal neuromorphic system that adopts a physical-model approach with a 1000-fold acceleration of spiking neural network dynamics relative to biological real time. Using the embedded plasticity processor, we both simulate the Pong arcade video game and implement a local plasticity rule that enables reinforcement learning, allowing the on-chip neural network to learn to play the game. The experiment demonstrates key aspects of the employed approach, such as accelerated and flexible learning, high energy efficiency and resilience to noise.
Active Goal Recognition
Venue: arXiv cs.AI 2019
Year: 2019

To coordinate with other systems, agents must be able to determine what the systems are currently doing and predict what they will be doing in the future---plan and goal recognition. There are many methods for plan and goal recognition, but they assume a passive observer that continually monitors the target system. Real-world domains, where information gathering has a cost (e.g., moving a camera or a robot, or time taken away from another task), will often require a more active observer. We propose to combine goal recognition with other observer tasks in order to obtain \emph{active goal recognition} (AGR). We discuss this problem and provide a model and preliminary experimental results for one form of this composite problem. As expected, the results show that optimal behavior in AGR problems balance information gathering with other actions (e.g., task completion) such as to achieve all tasks jointly and efficiently. We hope that our formulation opens the door for extensive further research on this interesting and realistic problem.
IoT Technologies for Augmented Human: a Survey
Venue: arXiv cs.NI 2019
Year: 2019

Internet of Things (IoT) technology has delivered new enablers for improving human abilities. These enablers promise an enhanced quality of life and professional efficiency; however, the synthesis of IoT and human augmentation technologies has also extended IoT-related challenges far beyond the current scope. These potential challenges associated with IoT-empowered Augmented Human (AH) have so far not been well-investigated. Thus, this article attempts to introduce readers to AH concept as well as summarize notable research challenges raised by such systems, in order to facilitate reader's further interest in this topic. The article considers emerging IoT applications for human augmentation, devices and design principles, connectivity demands, and security aspects.
Using human-in-the-loop synthesis to author functional reactive programs
Venue: arXiv cs.PL 2019
Year: 2019

Programs that respond to asynchronous events are challenging to write; they are difficult to reason about and tricky to test and debug. Because these programs can have a huge space of possible input timings and interleaving, the programmer may easily miss corner cases. We propose applying synthesis to aid programmers in creating programs more easily and with a higher degree of confidence in their correctness. We have written an efficient encoding of functional reactive programming (FRP) semantics based on functional programming over lists lifted in Rosette. We demonstrate that this technique is state-of-the-art by first comparing its performance against two existing synthesis tools that produce list manipulation programs, and then by synthesizing a suite of benchmarks given complete specifications. We also propose an interactive tool in which a programmer provides some initial partial specification in the form of input/output examples or invariants; the tool finds ambiguity in the specification by synthesizing two candidate programs and gives the user an input that distinguishes them; the user updates the specification and continues iterating until the correct program is found. As evaluation, we demonstrate the use of the tool on a suite of benchmarks from the web programming and Internet of Things domains and walk through a sample interaction on a realistic web benchmark, showing that we can converge on the target program with a tractable number of interactions. As future work, we discuss encoding additional FRP languages to in order to explore metalinguistic features, strategies for decomposition that would allow the synthesis of larger programs, and improved programmer tools such as a GUI to more easily elicit specifications.
Contact-Aware Controller Design for Complementarity Systems
Venue: arXiv cs.RO 2019
Year: 2019

While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, by exploiting the complementarity structure of contact dynamics, we propose a control framework which can close the loop on rich, tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on three different underactuated, multi-contact robotics problems.
Design Methodology for Energy Efficient Unmanned Aerial Vehicles
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we present a load-balancing approach to analyze and partition the UAV perception and navigation intelligence (PNI) code for parallel execution, as well as assigning each parallel computational task to a processing element in an Network-on-chip (NoC) architecture such that the total communication energy is minimized and congestion is reduced. First, we construct a data dependency graph (DDG) by converting the PNI high level program into Low Level Virtual Machine (LLVM) Intermediate Representation (IR). Second, we propose a scheduling algorithm to partition the PNI application into clusters such that (1) inter-cluster communication is minimized, (2) NoC energy is reduced and (3) the workloads of different cores are balanced for maximum parallel execution. Finally, an energy-aware mapping scheme is adopted to assign clusters onto tile-based NoCs. We validate this approach with a drone self-navigation application and the experimental results show that our optimal 32-core design achieves an average 82% energy savings and 4.7x performance speedup against the state-of-art flight controller.
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment
Venue: arXiv cs.CL 2019
Year: 2019

Reference corpus for word alignment is an important resource for developing and evaluating word alignment methods. For Myanmar-English language pairs, there is no reference corpus to evaluate the word alignment tasks. Therefore, we created the guidelines for Myanmar-English word alignment annotation between two languages over contrastive learning and built the Myanmar-English reference corpus consisting of verified alignments from Myanmar ALT of the Asian Language Treebank (ALT). This reference corpus contains confident labels sure (S) and possible (P) for word alignments which are used to test for the purpose of evaluation of the word alignments tasks. We discuss the most linking ambiguities to define consistent and systematic instructions to align manual words. We evaluated the results of annotators agreement using our reference corpus in terms of alignment error rate (AER) in word alignment tasks and discuss the words relationships in terms of BLEU scores.
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis
Venue: arXiv cs.CL 2019
Year: 2019

Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence. Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations. The attention is expected to concentrate on opinion words for accurate sentiment prediction. However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects. In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction. Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model. We further detect the opinion snippet by self-critical reinforcement learning. Especially, experimental results demonstrate the effectiveness of our method and prove that our hard-selection approach outperforms soft-selection approaches when handling multi-aspect sentences.
First Degree Entailment with Group Attitudes and Information Updates
Venue: arXiv cs.LO 2019
Year: 2019

We extend the epistemic logic with De Morgan negation by Fagin et al. (Artif. Intell. 79, 203-240, 1995) by adding operators for universal and common knowledge in a group of agents, and with a formalization of information update using a generalized version of the left division connective of the non-associative Lambek calculus. We provide sound and complete axiomatizations of the basic logic with the group operators and the basic logic with group operators and updates. Both logics are shown to be decidable.